BP神经网络对超临界CO_2萃取油茶籽油过程的模拟

Simulation of Supercritical CO_2 Extraction for Camellia Seed Oil by Back Propagation Neural Network

应用BP神经网络(BPNN)对超临界CO2萃取油茶籽油过程进行了模拟和预测。研究了神经网络的构建、训练以及学习算法和隐含层结构的优化,并用得到的神经网络对不同原料平均粒径(0.215～0.625 mm)、压力(30～35 MPa)、温度(35～50℃)、CO2流量(20～25 L/h)条件下的油茶籽油收率进行预测。结果表明:L-M算法是适宜的BP神经网络学习算法;具有5/8/1结构的BP神经网络的模拟性能最优;模型的预测值与实验结果吻合较好,大部分数据的相对误差小于3%,说明BP神经网络适用于超临界CO萃取油茶籽油过程的模拟。

Back propagation neural network（BPNN） was applied to simulate and predict the extraction of camellia seed oil by supercritical CO2.Neural network′s structure,training and optimization of learning algorithms and hidden layer structure were investigated.Under different conditions of feed particle′s mean diameter（0.215-0.625mm）,pressure（30-35MPa）,temperature（35-50℃） and CO2 flowrate（20-25L/h）,the yields of camellia seed oil were predicted by BPNN.The results show that L-M algorithm is the optimum learning algorithm of the BPNN,and the BPNN with 5/8/1 network structure has good simulating performance.The model predictions are in good agreement with the experimental data,and relative errors among most of the predictions and the corresponding experimental data are less than 3.0%.The BPNN developed can be used in simulation of this process.